On the other hand, data processing in WSNs may require consuming tasks to be performed at the microprocessor, much longer than the actual length of time a transceiver spends in transmit mode. This can cause a significant energy consumption by the microprocessor, even comparable to the energy consumed during transmission, or reception, by the transceiver. Thus, the general rule that the design of communication protocol design is much more important than that of the processing task scheduling is not always true. Some examples of network design are given in Sections 4.
The process of standardization in the field of WSNs is very active in the last years and an important outcome is represented by IEEE The main features of the We finally conclude the paper by giving our vision on future research directions in Section 7. A WSN can be defined as a network of devices, denoted as nodes , which can sense the environment and communicate the information gathered from the monitored field e.
The data is forwarded, possibly via multiple hops, to a sink sometimes denoted as controller or monitor that can use it locally or is connected to other networks e. The nodes can be stationary or moving. They can be aware of their location or not. They can be homogeneous or not.
Sensor Networks with IEEE 802.15.4 Systems: Distributed Processing, Mac, and Connectivity
This is a traditional single-sink WSN see Figure 1 , left part. Almost all scientific papers in the literature deal with such a definition. This single-sink scenario suffers from the lack of scalability: by increasing the number of nodes, the amount of data gathered by the sink increases and once its capacity is reached, the network size cannot be augmented. Moreover, for reasons related to MAC and routing aspects, network performance cannot be considered independent from the network size.
A more general scenario includes multiple sinks in the network see Figure 1 , right part [ 13 ]. Given a level of node density, a larger number of sinks will decrease the probability of isolated clusters of nodes that cannot deliver their data owing to unfortunate signal propagation conditions. In principle, a multiple-sink WSN can be scalable i. However, a multi-sink WSN does not represent a trivial extension of a single-sink case for the network engineer.
In many cases nodes send the data collected to one of the sinks, selected among many, which forward the data to the gateway, toward the final user see Figure 1 , right part. From the protocol viewpoint, this means that a selection can be done, based on a suitable criterium that could be, for example, minimum delay, maximum throughput, minimum number of hops, etc. Therefore, the presence of multiple sinks ensures better network performance with respect to the single-sink case assuming the same number of nodes is deployed over the same area , but the communication protocols must be more complex and should be designed according to suitable criteria.
The variety of possible applications of WSNs to the real world is practically unlimited, from environmental monitoring [ 14 ], health care [ 15 ], positioning and tracking [ 16 ], to logistic, localization, and so on. A possible classification for applications is provided in this section. It is important to underline that the application strongly affects the choice of the wireless technology to be used. Once application requirements are set, in fact, the designer has to select the technology which allows to satisfy these requirements.
To this aim the knowledge of the features, advantages and disadvantages of the different technologies is fundamental. Owing to the importance of the relationship between application requirements and technologies, we report in this Section some example requirements and we devoted Sections 5 and 6 to an overview of the main features of the most promising technologies provided for WSNs.
One of the possible classifications distinguishes applications according to the type of data that must be gathered in the network. Almost any application, in fact, could be classified into two categories: event detection ED and spatial process estimation SPE. In the first case sensors are deployed to detect an event, for example a fire in a forest, a quake, etc.
Signal processing within devices is very simple, owing to the fact that each device has to compare the measured quantity with a given threshold and to send the binary information to the sink s.
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The density of nodes must ensure that the event is detected and forwarded to the sink s with a suitable probability of success while maintaining a low probability of false alarm. The detection of the phenomenon of interest POI could be performed in a decentralized or distributed way, meaning that sensors, together with the sink, cooperatively undertake the task of identifying the POI. However, unlike in classical decentralized detection problems, greater challenges exist in a WSN setting. There are stringent power constraints for each node, communication channels between nodes and the fusion center are severely bandwidth-constrained and are no longer lossless e.
In the context of decentralized detection, cooperation allows exchange of information among sensor nodes to continuously update their local decisions until consensus is reached across the nodes. In this case the main issue is to obtain the estimation of the entire behavior of the spatial process based on the samples taken by sensors that are typically placed in random positions [ 20 — 23 ]. The measurements will then subject to proper processing which might be performed either in a distributed manner by the nodes, or centrally at the supervisor.
The estimation error is strictly related to nodes density as well as on the spatial variability of the process. Higher nodes density lead to a more accurate scalar field reconstruction at the expense of a larger network throughput and cost. In the recent literature, different works addressed the estimation of a scalar field using random WSNs. As an example, [ 20 ] presents a distributed algorithm able to estimate the gradient of a generic smooth physical process energy constraints and nodes failure are not considered there ; in [ 21 ] the relationship between the random topology of a sensor network and the quality of the reconstructed field is investigated and some guidelines on how sensors should be deployed over a spatial area for efficient data acquisition and reconstruction are derived.
Distributed source coding techniques can be successfully exploited to reduce the amount of data to be transmitted and hence to improve the network energy efficiency [ 24 ]. There exist also applications that belong to both categories. To the first category belong, for example, the location of a fire in a forest, or the detection of a quake, etc. Alternatively, the estimation of the temperature of a given area belongs to the second category.
In general, these applications aim at monitoring indoor or outdoor environments, where the supervised area may be few hundreds of square meters or thousands of square kilometers, and the duration of the supervision may last for years. Natural disasters such as floods, forest fires, earthquakes may be perceived earlier by installing networked embedded systems closer to places where these phenomena may occur. Such systems cannot rely on a fixed infrastructure and have to be very robust, because of the inevitable impairments encountered in open environments.
The system should respond to environment changes as quick as possible. The environment to be observed will mostly be inaccessible by the human all the time. Hence, robustness plays an important role. Also security and surveillance applications have some demanding and challenging requirements such as real-time monitoring and high security. An other application that could belong to both the above defined categories is devoted to the realisation of energy efficient buildings. In this application, in fact, sensor nodes could aim at estimating a process SPE , but also events ED. In this case the WSN is distributed in buildings residential or not to manage efficiently the energy consumption of all the electric appliances.
Consequently, nodes have to continuously monitor the energy consumed by all appliances connected to the electrical grid. Therefore, sensors have to estimate a process, that is the energy consumption which varies with time, but in some cases, they could be used to detect some events. As an example, sensors could detect the arrival of a person in a room to switch on some electrical appliances.
The project, in fact, aims at achieving energy efficient buildings through innovative solutions based on networked embedded systems. The eDIANA approach is to achieve greater efficiency in the use of resources, prioritizing energy as scarce resource, more flexibility in the provision of resources and better situation awareness for the citizen and for service and infrastructure owners. This will be achieved through the deployment and inter-operation of embedded systems throughout the eDIANA environment of buildings and intra-building units.
Due to the wide variety of possible applications of WSNs, system requirements could change significantly. For instance, in case of environmental monitoring applications, the following requirements are typically dominant: energy efficiency , nodes are battery powered or have a limited power supply; low data rate , typically the amount of data to be sensed is limited; one-way communication , nodes act only as sensors and hence the data flow is from nodes to sink s ; wireless backbone , usually in environmental monitoring no wired connections are available to connect sink s to the fixed network.
Significantly different are the requirements of a typical industrial application where wireless nodes are used for cable replacement: reliability , communication must be robust to failure and interference; security , communication must be robust to intentional attacks; inter-operability , standards are required; high data rate , the process to be monitored usually carries a large amount of data; two-way communication , in industrial applications nodes typically act also as actuators and hence the communication between sink s and nodes must be guaranteed; wired backbone , sinks can be connected directly to the fixed network using wired connections.
Even if requirements are strongly application dependent, one of the most important issues in the design of WSNs, especially in such scenarios where power supply availability is limited, is energy efficiency. High energy efficiency means long network lifetime and limited network deployment and maintenance costs. Energy efficiency can be achieved at different levels starting from the technology level e. For example, at physical and MAC layers, nodes could operate with low duty cycle by spending most of their time in sleeping mode to save energy.
This poses new problems such as that nodes may not wake up at the same time, due to the drifts of their local clocks, thus making the communication impossible. Suitable network synchronization schemes are mandatory in this case [ 8 , 25 ]. The main features of WSNs, as could be deduced by the general description given in the previous sections, are: scalability with respect to the number of nodes in the network, self-organization, self-healing, energy efficiency, a sufficient degree of connectivity among nodes, low-complexity, low cost and size of nodes.
Those protocol architectures and technical solutions providing such features can be considered as a potential framework for the creation of these networks, but, unfortunately, the definition of such a protocol architecture and technical solution is not simple, and the research still needs to work on it [ 5 ]. The massive research on WSNs started after the year However, it took advantage of the outcome of the research on wireless networks performed since the second half of the previous century. In particular, the study of ad hoc networks attracted a lot of attention for several decades, and some researchers tried to report their skills acquired in the field of ad hoc networks, to the study of WSNs.
According to some general definitions, wireless ad hoc networks are formed dynamically by an autonomous system of nodes connected via wireless links without using an existing network infrastructure or centralized administration. Apparently, this definition can include WSNs. However, this is not true. Apart from the very first item, which is common to WSNs, in all other cases there is a clear distinction between WSNs and wireless ad hoc networks. In WSNs, nodes are simple and low-complexity devices; the typical applications require few bytes sent periodically or upon request or according to some external event; every node can be either source or destination of information, not both; some nodes do not play the role of routers; energy efficiency is a very relevant matter, while capacity is not for most applications.
Therefore, WSNs are not a special case of wireless ad hoc networks. Thus, a lot of care must be used when considering protocols and algorithms which are good for ad hoc networks, and using them in the context of WSNs. Owing to the plethora of features, WSNs design involves a wide range of aspects and considerations and often imposes that several issues, like connectivity, access to the channel, signal processing techniques, etc.
As an example, in [ 24 ] a self-organizing single-sink WSN, enabling environmental monitoring through the estimate of a scalar field over a bi-dimensional scenario, is considered. Nodes are assumed to be distributed according to a Poisson point process PPP over the area and are organized in a cluster-based topology.
Connectivity issues, randomness of the channel, MAC issues and the role of distributed digital signal processing DDSP techniques are jointly accounted for, in a mathematical framework developed in the paper. Owing to the requirement of low device complexity together with low energy consumption i. The adoption of DDSP techniques aims at reducing the amount of transmitted data over the wireless medium; on the other hand, the complexity of the signal processing performed at a single node has to be kept under control [ 26 — 28 ]. In [ 24 ] the possibility that nodes perform DDSP is studied through a distributed compression technique based on signal re-sampling.
The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the sink. The model developed allows the analysis of the network under two different perspectives: the estimation of the process and the energy consumption. The trade-off between energy conservation and estimation error is discussed and a design criterion proposed. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics.
The main goal of [ 24 ] is neither to design specific communication protocols, nor DDSP techniques; rather, the joint consideration of all aspects mentioned, under realistic but simple working conditions, aims at stressing their interdependencies in a formalized framework. Therefore, being the goal of [ 24 ], the proposal of a new approach for designing WSNs suffers the following limits: i a single-sink scenario and not the more general multi-sink scenario, is accounted for; ii the MAC protocol is very simple slotted ALOHA , and no reference to any specific standard air interface is provided.
In [ 29 — 31 ] a multi-sink WSN, collecting data from the environment through the sampling of some physical entities and sending them to some external user, through multiple sinks, is considered. Through a simple polling model, sinks periodically issue queries, causing all sensors perform sensing and communicating their measurement results back to the sinks they are associated with. IEEE This paper introduces the concept of area throughput , that is, the amount of data per second successfully transmitted to the sinks from a given area. This performance metric is strictly related to both connectivity and MAC issues: it depends, in fact, on the probability that a given sensor node is not isolated and that it succeeds in transmitting its packet i.
In this section a low-cost hardware and software WSN test-bed developed for agricultural monitoring is described. The platform has been designed to provide the farmer with a periodic and punctual monitoring of physical parameters e. Thanks to this information it is possible to increase the quality and amount of production, cut costs, and reduce the pollution caused by weed-killers. The test-bed developed in cooperation with the start-up company SeNet s. Each node is composed of an IEEE Each node emits a maximum power equal to 0 dBm and it is capable of communicating with neighbor nodes up to a maximum distance of meters in line-of-sight conditions.
The communication protocol has been designed to allow an extremely low duty cycle where nodes wake up for 10 seconds activity mode at intervals of 15 minutes. During the activity mode, each node collects measured data from its sensors and transmits the information to the sink node. In case the sink node is out of the radio link range, data are forwarded by intermediate nodes, which act as relays, in a multi-hop fashion according to a mesh network topology.
To this purpose, a robust ad hoc network synchronization protocol has been designed to compensate relative clock drifts among nodes thus avoiding that nodes wake up in not overlapped time intervals. In sleeping mode, node consumption is only 0. The resulting network lifetime is in the order of several weeks in the absence of photovoltaic panels and it is limited only by the rechargeable battery lifetime when photovoltaic panels are present.
All sensed data collected by the sink node coordinator are then forwarded to the Internet every 2 hours through a GPRS link. The periodic report as well as each node status can be examined through a remote standard Internet connection. In Figure 3 an example of data report related to the temperature behavior measured in June is shown.
Screenshot of temperature behavior measured during June by 2 different nodes red and blue curves, respectively. Being energy efficiency one of the most important requirement for this application, we show the behavior of the battery status. In particular, in Figure 4 we show the battery charge in Volt as a function of time, expressed in hours, when photovoltaic panels are used and not. Two square panels with side of 10 cm are used. The behavior of the battery charge in Volt by passing time, expressed in hours, when photovoltaic panels are used and not.
The key features of The main field of application of this technology is the implementation of WSNs. The IEEE In the following, some technical details related to the physical and MAC layers as defined in the standard are reported.
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Finally some characteristics related to higher layers will be presented, considering Zigbee and 6LowPan, with particular attention to the former. The However, spread spectrum techniques are wherever mandatory to reduce the interference level in shared unlicensed bands. The ideal transmission range is computed considering that, although any legally acceptable power is permitted, IEEE According to the energy efficiency issue, low rate and low duty cycle are provided. The algorithm is implemented using units of time called backoff periods.
First, each node will delay any activities for a random number of backoff periods. After this delay, channel sensing is performed for one unit of time: if the channel is found free the node immediately starts the transmission; if, instead, the channel is busy the node enters again in the backoff state. There exists a maximum number of time the node can try to access the channel i. When this maximum is reached, the algorithm ends and the transmission cannot occur.
In the beacon-enabled mode [ 12 ], instead, the access to the channel is managed through a superframe, starting with a packet, called beacon , transmitted by WPAN coordinator. The use of GTSs is optional. BO defines the interval of time between two successive beacons, namely the beacon interval, denoted as BI ; its duration is equal to. This is, in fact, the minimum interval of time that must be guaranteed between the reception of two subsequent packets. The minimum CAP duration is equal to T s. The other difference with the non beacon-enabled case is that backoff period boundaries of every node in the WPAN must be aligned with the superframe slot boundaries of the coordinator; therefore, the beginning of the first backoff period of each node is aligned with the beginning of the beacon transmission.
Moreover, all transmissions may start on the boundary of a backoff period. To overcome the limited transmission range, multi-hop self-organizing network topologies are required. These can be realized taking into account that IEEE Two basic topologies are allowed, but not completely described by the standard since definition of higher layers functionalities are out of the scope of An example of both the IEEE Star topology is preferable in case coverage area is small and low latency is required by the application.
In this topology, communication is controlled by the PAN coordinator that acts as network master, sending packets, named beacons for synchronization and managing device association. Network devices are allowed to communicate only with the PAN coordinator and any FFD may establish its own network by becoming a PAN coordinator according to a predefined policy. A network device that wishes to join a star network listens for a beacon message, and after receiving it, the network device can send an association request back to the PAN coordinator, which either allows or denies the association.
Star networks also support a non beacon-enabled mode. In this case, beacons are used for association purpose only, whereas synchronization is achieved by polling the PAN coordinator for data on a periodic basis. Star networks operate independently from their neighboring networks.
Peer-to-peer topology is preferable in case a large area should be covered and latency is not a critical issue. This topology allows the formation of more complex networks and permits any FFD to communicate with any other FFD behind its transmission range via multi-hop. Each device in a peer-to-peer structure needs to proactively search for other network devices.
Once a device is found, the two devices can exchange parameters to recognize the type of services and features each supports. However, the introduction of multihop requires additional device memory for routing tables. These last network topology options are not part of the IEEE All devices belonging to a particular network, regardless of the type of topology, use their unique IEEE bit addresses and a short bit address is allocated by the PAN coordinator to uniquely identify the network.
A PAN coordinator wishing to establish a new PAN needs to find a channel free from the interference that would render the channel unsuitable e. The channel selection is performed by the PAN coordinator through the Energy Detection ED scan which returns the measure of the peak energy in each channel. It must be noticed that the standard only provides the ED mechanism, and it does not specify the channel-selection logic.
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The discovery of available PANs is performed by scanning beacon frames broadcasted by the coordinators. Two different types of scan that can be used in the association phase are proposed:. After the scan of the channels, a list of available PANs is used by the device to choose the network to try to connect with.
In the standard, no specific procedure to select a PAN is provided and so, this selection among potential parents is open for different implementations. Hence, the device sends an association request frame to the coordinator device by means of which the selected network was discovered. The association phase ends with a successful association response command frame to the requesting device. This procedure basically results in a set of MAC association relationships between devices, named in the following parent-child relationship.
ZigBee defines the network and application layers above the Zigbee is being promoted by the Zigbee Alliance and its main contribution is giving mesh network capabilities to Mesh networking allows reconfiguration around blocked paths by hopping from node to node until the data reaches the destination. Moreover, Zigbee specifications define a beacon-enabled tree-based topology, as a particular case of the IEEE This topology, depicted in Figure 8 as an example, can be interpreted as a hierarchical tree where nodes at a given level transmit data to nodes at a lower level, to reach the PAN coordinator, which is the root of the tree.
Only one device in tree assumes the role of PAN coordinator, that is generally the sink of the scenario. In case of multi-sink scenario more PAN coordinators are present and a forest of disjoint trees, rooted at the PAN coordinators is established. Two different types of nodes are present in the tree: the routers, that must be FFDs, which receive data from their children, aggregate them, and transmit the packet obtained to their parents; and the leafs, that could be FFDs or RFDs, which have no routing functionalities and have only to transmit their packets to the parent.
The topology formation procedure is started by the PAN coordinator, which broadcasts beacon packets to neighbour nodes. A candidate node receiving the beacon may request to join the network at the PAN coordinator.
If the PAN coordinator allows the node to join, it will begin transmitting periodic beacons so that other candidate nodes may join the network. As stated above, nodes must be in beacon-enabled mode: each child node tracks the beacon of its parent see Figure 9 , where the tracking period is outlined as a dashed rectangle. A core concept of this tree topology is that the child node may transmit its own beacon at a predefined offset with respect to the beginning of its parent beacon: the offset must always be larger than the parent superframe duration and smaller than beacon interval see Figure 9.
This implies that the beacon and the active part of child superframe reside in the inactive period of the parent superframe; therefore, there is no overlap at all between the active portions of the superframes of child and parent.
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This concept can be expanded to cover more than two nodes: the selected offset must not result in beacon collisions with neighbouring nodes. This implies that the node must record the time stamp of all neighbouring nodes and selects a free time slot for its own beacon.
Obviously a child will transmit a beacon packet only when it is a router in the tree; if the child is a leaf it has only to transmit the packet to its parent. Therefore, each router in the tree, after the reception of the beacon coming from the parent, will select the instant in which transmits its beacon. Beacon scheduling is necessary to prevent the beacon frames of one device from colliding with either the beacon frames or data transmissions of its neighboring devices. An example of superframe structure in 3-level tree, having two routers at level one, is shown in Figure Routers at level 1 transmit the beacon and define superframes that are not overlapped and all contained in the inactive part of the PAN coordinator superframe.
Some key technologies of 6LowPAN are as follows [ 33 ]. The decision to select one standard versus another should be determined by the target application. For an application in which there is no need to interface with IP devices or the packet size is small, it is not necessary to implement 6LowPAN, which performs fragmentation. Zigbee can achieve better overall performance in such an application.
In this section some examples of performance trends obtained from the study of IEEE The aim is to provide some numerical results in terms of throughput and energy consumption and to show how the choice of the topology affects performance in WSNs. In Figure 11 experimental results are shown. A point-to-point network, where a source node has to transmit data to a destination node, possibly through a number of routers, is considered. When one router between the source and the destination is present, a two-hop communication is performed; in case of two routers we have three hops, etc.
The figure shows the behavior of the throughput, that is the number of bits of the MAC payload per second successfully received by the final destination, as a function of the payload size. However, the throughput can be significantly lowered owing to the potentially interference among the separate hops disturbing each other. Throughput measured for a point-to-point In Figure 12 a single-sink scenario where A network composed of 30 nodes, working in beacon-enabled mode is accounted for.
In the figure we show the throughput as a function of the size of the packets transmitted by nodes. The throughput here represents the number of bits of the payload per second correctly received by the sink when all the 30 nodes try to access the channel and transmit their packets, assuming that nodes transmit packets of the same size. Throughput as a function of packet size for an For a fair comparison in terms of delay, curves obtained by setting the same value of BO should be considered meaning that the superframes have the same whole duration.
As we can see, for low values of the packet size, stars outperform trees, whereas trees perform better when the packet size increases, since less nodes compete to access the channel at the same time nodes are split in two levels. This is, in fact, for the network chosen with 30 nodes and a mean number of 3 level one nodes the best compromise between the duration of the active part of the sink superframe where level one nodes transmit and that of the inactive part where level 1 superframes are located.
The increase of the number of nodes on one hand increases the amount of data transmitted toward the sink per unit of time, but, on the other, the success probability, that is the probability that a node succeeds in transmitting correctly the packet, decreases [ 36 ]. Finally, in Figure 13 the mean energy spent by a single node having a packet of a given size to be transmitted, is shown. We assume that the network is composed of N The model developed in [ 35 ] is used.
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The behavior of the mean energy spent as a function of the packet size, for different values of N , is shown. As we can see, the mean energy spent presents a maximum. This is due to the fact that for low packet sizes, the increase of the size increases the energy as well, since a larger amount of energy is spent for transmitting larger packets.
Conversely, when the packet size becomes too large, the energy starts to decrease since the probability that the node succeeds in accessing the channel and transmitting its packet gets lower as well. The mean energy spent by an Ultrawide bandwidth radio is a fast emerging technology with uniquely attractive features that has attracted a great deal of interest from academia, industry, and global standardization.
This article will describe characteristics of IEEE This implementation supports both the cloud and fog models. The IEEE standards family is broken out into a number of task groups including In particular, IEEE The It leaves the upper layers to the implementer. Each of these implements the remainder of the OSI protocol model to deliver services such as routing and discovery as well as APIs for user applications. In general, Even lower data rates are achievable to further limit power consumption.
In spite of the meter foot range specification, in the 2. In the sub-GHz frequencies, practical implementations of the protocol have been demonstrated at ranges of over 6. At the physical layer, IEEE There are six PHYs currently defined, depending on the frequency range and data performance required. The frame size for